code: def prepare_batch_at_step(
      self,
      tokens: torch.Tensor,
      maxlen: int,
      micro_batch_size: int,
      step: int,
      context_length: int,
  ) -> Tuple[List[torch.Tensor], List[int]]:
      tokenizer = self.model.tokenizer
  
      if context_length % 64 == 0:
          # added a new retrieval context
          token_context = tokens[
              :, context_length - 64 : context_length
          ]
          chunks = self.service.get_knn(token_context, self.neighbors)
          if self.store_retrieved:
              self._store_retrieved(token_context, chunks)
          self.retrieved.append(chunks)
      elif self.frequent_query and len(self.retrieved) > 0:
          token_context = tokens[
              :, context_length - 64 : context_length
          ]
          chunks = self.service.get_knn(token_context, self.neighbors)
          if self.store_retrieved:
              self._store_retrieved(token_context, chunks)
          self.retrieved[-1] = chunks
  
      # types2use = None
      if step == 0:
          # Allocate memory for the entire context.
          set_inference_key_value_memory = True
          tokens2use = tokens[:, :context_length]
          positions2use = self.position_ids[:, :context_length]
          # not using type2use. uncomment it if it is used
          # if type_ids is not None:
          #     types2use = type_ids[:, :context_length]
      else:
          # Set this to false so the memory is not reallocated.
          set_inference_key_value_memory = False
          tokens2use = tokens[:, context_length - 1].view(
              micro_batch_size, -1
          )
          positions2use = self.position_ids[:, context_length - 1].view(
              micro_batch_size, -1
          )
          # not using type2use. uncomment it if it is used
          # if type_ids is not None:
          #     types2use = type_ids[:, context_length - 1].view(batch_size, -1)
      retrieved = torch.tensor(
          np.array(self.retrieved), device=torch.cuda.current_device()
      )
      if retrieved.numel() != 0:
          retrieved = retrieved.transpose(0, 1).contiguous()
      if self.megatron_lm_compatible:
          # all TRUE for megatron lm, there is no attention mask
          retrieved_mask = torch.ones_like(retrieved, dtype=torch.bool)
      else:
          retrieved_mask = retrieved != tokenizer.pad_id
      if retrieved.numel() == 0:
          # add empty retrieved
          retrieved = (
              torch.tensor(
                  self.service.get_knn(["a"], 0),
                  device=torch.cuda.current_device(),
              )
              .unsqueeze(0)
              .repeat(1, len(self.retrieved), 1, 1)
          )
          retrieved_mask = retrieved != tokenizer.pad_id
          # retrieved = torch.tensor([-1] * micro_batch_size)
          # retrieved_mask = torch.tensor([-1] * micro_batch_size)
  
      """Prepare batch for each of the inference steps"""
      # attention_mask_repeat = torch.concat([self.attention_mask for _ in range(micro_batch_size)])
      setkey_value_array = torch.tensor(
          [set_inference_key_value_memory] * micro_batch_size,
          device=torch.cuda.current_device(),
      )
      len_array = torch.tensor(
          [maxlen] * micro_batch_size,
          device=torch.cuda.current_device(),
      )
      if self.neighbors == 0:
          # no retrieval, use 1 padding
          neighbors_array = torch.tensor(
              [1] * micro_batch_size, device=torch.cuda.current_device()
          )
      else:
          neighbors_array = torch.tensor(
              [self.neighbors] * micro_batch_size,
              device=torch.cuda.current_device(),
          )
  
      batch = [
          tokens2use,
          self.attention_mask[:, :context_length],
          retrieved,
          retrieved_mask,
          setkey_value_array,
          len_array,
          neighbors_array,
          positions2use,
      ]
      tensor_shape = [
          tokens2use.shape[1],
          micro_batch_size,
          self.model.cfg.hidden_size,
      ]
      return batch, tensor_shape
  
imports: import json
  import pickle
  from typing import List, Tuple
  import numpy
  import torch
  import torch.distributed
  from nemo.collections.nlp.modules.common.lm_utils import pad_batch
  from nemo.collections.nlp.modules.common.megatron.retrieval_services.retrieval_service import ComboRetrievalService
  from nemo.collections.nlp.modules.common.text_generation_strategy import TextGenerationStrategy
label_desc: Use image node_id 9 for calling the RetroModelTextGenerationStrategy obj's underlying member method code with example usage: obj.prepare_batch_at_step(tokens, maxlen, micro_batch_size, step, context_length) and returns: batch,  tensor_shape